Research progress of artificial intelligence in glaucoma

Currently, the early diagnosis of glaucoma and monitoring of disease progression is difficult and requires assessment of structural(fundus photo/ optical coherence tomography scan)and functional damage(visual fields)of the optic nerve head(ONH). It requires the clinical knowledge of glaucoma experts...

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Autores principales:	Chao-Xu Qian, Hua Zhong
Formato:	article
Lenguaje:	EN
Publicado:	Press of International Journal of Ophthalmology (IJO PRESS) 2021
Materias:	artificial intelligence deep learning glaucoma fundus photography optical coherence tomography visual field Ophthalmology RE1-994
Acceso en línea:	https://doaj.org/article/e7808b52b3eb4b428da113133882969a
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spelling	oai:doaj.org-article:e7808b52b3eb4b428da113133882969a2021-11-22T14:22:32ZResearch progress of artificial intelligence in glaucoma1672-512310.3980/j.issn.1672-5123.2021.12.11https://doaj.org/article/e7808b52b3eb4b428da113133882969a2021-12-01T00:00:00Zhttp://ies.ijo.cn/cn_publish/2021/12/202112011.pdfhttps://doaj.org/toc/1672-5123Currently, the early diagnosis of glaucoma and monitoring of disease progression is difficult and requires assessment of structural(fundus photo/ optical coherence tomography scan)and functional damage(visual fields)of the optic nerve head(ONH). It requires the clinical knowledge of glaucoma experts and is highly labor intensive. Artificial intelligence(AI)applications have been proposed to improve the understanding of glaucoma and help to reduce the time and manpower required for such clinical tasks. With the advent of deep learning(DL), many tools for ophthalmological image enhancement, segmentation and classification have also emerged. Especially in the last three years, a large number of algorithms suitable for analyzing the ONH structure and/or function, which have been proposed to help in glaucoma detection. AI tools have also been developed to predict the early progression of the disease. Bring the possibility of personalized precision treatment. However, these algorithms are yet to be tested in the real world. This review summarizes the diverse landscape of AI algorithms developed for glaucoma. We also discuss the current limitations and challenges that we need to overcome.Chao-Xu QianHua ZhongPress of International Journal of Ophthalmology (IJO PRESS)articleartificial intelligencedeep learningglaucomafundus photographyoptical coherence tomographyvisual fieldOphthalmologyRE1-994ENGuoji Yanke Zazhi, Vol 21, Iss 12, Pp 2081-2085 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence deep learning glaucoma fundus photography optical coherence tomography visual field Ophthalmology RE1-994
spellingShingle	artificial intelligence deep learning glaucoma fundus photography optical coherence tomography visual field Ophthalmology RE1-994 Chao-Xu Qian Hua Zhong Research progress of artificial intelligence in glaucoma
description	Currently, the early diagnosis of glaucoma and monitoring of disease progression is difficult and requires assessment of structural(fundus photo/ optical coherence tomography scan)and functional damage(visual fields)of the optic nerve head(ONH). It requires the clinical knowledge of glaucoma experts and is highly labor intensive. Artificial intelligence(AI)applications have been proposed to improve the understanding of glaucoma and help to reduce the time and manpower required for such clinical tasks. With the advent of deep learning(DL), many tools for ophthalmological image enhancement, segmentation and classification have also emerged. Especially in the last three years, a large number of algorithms suitable for analyzing the ONH structure and/or function, which have been proposed to help in glaucoma detection. AI tools have also been developed to predict the early progression of the disease. Bring the possibility of personalized precision treatment. However, these algorithms are yet to be tested in the real world. This review summarizes the diverse landscape of AI algorithms developed for glaucoma. We also discuss the current limitations and challenges that we need to overcome.
format	article
author	Chao-Xu Qian Hua Zhong
author_facet	Chao-Xu Qian Hua Zhong
author_sort	Chao-Xu Qian
title	Research progress of artificial intelligence in glaucoma
title_short	Research progress of artificial intelligence in glaucoma
title_full	Research progress of artificial intelligence in glaucoma
title_fullStr	Research progress of artificial intelligence in glaucoma
title_full_unstemmed	Research progress of artificial intelligence in glaucoma
title_sort	research progress of artificial intelligence in glaucoma
publisher	Press of International Journal of Ophthalmology (IJO PRESS)
publishDate	2021
url	https://doaj.org/article/e7808b52b3eb4b428da113133882969a
work_keys_str_mv	AT chaoxuqian researchprogressofartificialintelligenceinglaucoma AT huazhong researchprogressofartificialintelligenceinglaucoma
_version_	1718417587665108992

Research progress of artificial intelligence in glaucoma

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